Name
Abstract | ||
|---|---|---|
Limitations of state-of-the-art teleoperation systems can be compensated by using shared-control teleoperation architectures that provide haptic assistance to the human operator. This paper presents a new approach for computer-assisted teleoperation, which recognizes human intentions and dependent on the classified task activates different types of assistances. For this purpose, time series haptic data is recorded during interaction, passed through an event-based feature extraction, and finally used for task classification by applying a Hidden Markov Model approach. The effect of the assistance function on human behavior is discussed and taken into account by training multiple classifiers for each type of assistance. The introduced approach is finally validated in a real hardware experiment. Results show an accurate intention recognition for assisted and non-assisted teleoperation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14064-8_34 | EuroHaptics |
Keywords | Field | DocType |
human intention,human behavior,human operator,haptic assistance,assistance function,computer-assisted teleoperation system,non-assisted teleoperation,computer-assisted teleoperation,online intention recognition,hidden markov model approach,state-of-the-art teleoperation system,shared-control teleoperation architecture,feature extraction,time series,hidden markov model | Teleoperation,Computer vision,Human operator,Computer science,Feature extraction,Artificial intelligence,Hidden Markov model,Haptic technology | Conference |
Volume | ISSN | ISBN |
6191 | 0302-9743 | 3-642-14063-7 |
Citations | PageRank | References |
2 | 0.37 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolay Stefanov | 1 | 44 | 3.88 |
| Angelika Peer | 2 | 406 | 40.39 |
| Martin Buss | 3 | 1799 | 159.02 |